Information processing method, information processing device, and recording medium

ABSTRACT

An information processing device obtains measurement data for calculating biological information of a measurement subject from a sensor that performs measurement without contact. Based on content of the measurement data, the measurement data is classified into a group associated with at least a position of the measurement subject, the biological information is calculated from the classified measurement data, and the calculated biological information is compared with a reference value associated with a group to which the measurement data belongs. Then, a notification is made based on the comparing.

BACKGROUND 1. Technical Field

The present disclosure relates to a technique for grasping a biologicalstate of a measurement subject.

2. Description of the Related Art

In recent years, techniques for sensing biological information of ameasurement subject without contact are being developed. For example, inJapanese Unexamined Patent Application Publication No. 2009-119896,there is disclosed an air conditioner whose objective is to performs airconditioning in such a way that a person staying in a room feelscomfortable. The air conditioner obtains a captured image of a room anda temperature distribution image in the room, calculates a face positionbased on the captured image, obtains, from the temperature distributionimage, temperature information at a location separated from the faceposition by a predetermined distance, and performs air conditioningaccording to the obtained temperature information.

In Japanese Unexamined Patent Application Publication No. 2015-55393,there is disclosed the following technique, and an objective thereof isto stably calculate the temperature of a crew's face by considering thedirection of the crew's face. That is, Japanese Unexamined PatentApplication Publication No. 2015-55393 discloses a technique in which aface region of a crew is extracted from a temperature distribution mapobtained by an infrared (IR) camera, contribution ratios of a centerregion of the face region and surrounding regions positioned at rightand left sides of the center region are adjusted so that the area of thecenter region and the area of the surrounding region become equal, and aface temperature of the crew is calculated based on the adjustedcontribution ratios.

SUMMARY

However, in related art, there are cases where the biological state of ameasurement subject is difficult to grasp correctly. The state of bloodflow in the body differs from person to person, and it is known that,for example, in a human body with one-sided paralysis, the bodytemperature on paralysis side is substantially low. Accordingly, it isdifficult to correctly grasp a change in body temperature of a humanbody only by measuring the body temperature of a face part whileadjusting the contribution ratios of the center region and thesurrounding regions so that the area of the center region and the areaof the surrounding region become equal, as disclosed in JapaneseUnexamined Patent Application Publication No. 2015-55393.

The invention disclosed in Japanese Unexamined Patent ApplicationPublication No. 2009-119896 detects the temperature at a locationseparated from the face position by a predetermined distance. Thus, itis difficult to correctly grasp the body temperature of a human bodyitself.

One non-limiting and exemplary embodiment provides a technique forcorrectly grasping a biological state of a measurement subject.

In one general aspect, the techniques disclosed here features aninformation processing method comprising: obtaining measurement data forcalculating biological information of a measurement subject from asensor that performs measurement without contact; based on content ofthe measurement data, classifying the measurement data into a groupassociated with at least a position of the measurement subject,calculating the biological information from classified measurement data,and comparing calculated biological information with a reference valueassociated with a group to which the measurement data belongs; andmaking a notification based on the comparing.

An embodiment of the present disclosure enables correct grasping of abiological state of a measurement subject.

It should be noted that general or specific embodiments may beimplemented as a system, a method, an integrated circuit, a computerprogram, a storage medium, or any selective combination thereof.

Additional benefits and advantages of the disclosed embodiments willbecome apparent from the specification and drawings. The benefits and/oradvantages may be individually obtained by the various embodiments andfeatures of the specification and drawings, which need not all beprovided in order to obtain one or more of such benefits and/oradvantages.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a diagram illustrating an overview of a biological informationsensing device to which an information processing device according toEmbodiment 1 is applied;

FIG. 2 is a diagram illustrating one example of connection configurationof the biological information sensing device;

FIG. 3 is a diagram illustrating a configuration of functions of abiological information sensing device to which an information processingdevice according to Embodiment 1 is applied;

FIG. 4 is a diagram illustrating a data processing method in a learningphase;

FIG. 5 is a diagram illustrating a data processing method in a detectionphase;

FIG. 6 is a diagram that follows FIG. 5 and illustrates the dataprocessing method in the detection phase;

FIG. 7 is a flowchart illustrating one example of a process of aninformation processing device in the detection phase;

FIG. 8 is a diagram illustrating an overview of a biological informationsensing device to which an information processing device according toEmbodiment 2 is applied;

FIG. 9 is a diagram illustrating one example of a functionalconfiguration of a biological information sensing device to which aninformation processing device according to Embodiment 2 is applied;

FIG. 10 is a diagram illustrating a data processing method in a learningphase;

FIG. 11 is a diagram illustrating a data processing method in adetection phase;

FIG. 12 is a diagram that follows FIG. 11 and illustrates the dataprocessing method in the detection phase;

FIG. 13 is a diagram illustrating an overview of a biologicalinformation sensing device to which an information processing deviceaccording to a modified example 2 of the present disclosure is applied;

FIG. 14 is a diagram illustrating a configuration of functions of abiological information sensing device to which an information processingdevice according to a modified example 3 of the present disclosure isapplied;

FIG. 15 is a diagram illustrating one example of a connectionconfiguration of a biological information sensing device to which aninformation processing device according to the modified example 3 of thepresent disclosure is applied;

FIG. 16 is a diagram illustrating a configuration of functions of abiological information sensing device to which an information processingdevice according to a modified example 4 of the present disclosure isapplied;

FIG. 17 is a diagram illustrating a configuration of functions of abiological information sensing device to which an information processingdevice according to a modified example 5 of the present disclosure isapplied;

FIG. 18 is a diagram illustrating a first example of a connectionconfiguration of a biological information sensing device to which aninformation processing device according to the modified example 5 of thepresent disclosure is applied;

FIG. 19 is a diagram illustrating a second example of a connectionconfiguration of a biological information sensing device to which aninformation processing device according to the modified example 5 of thepresent disclosure is applied;

FIG. 20 is a diagram illustrating a connection configuration of abiological information sensing device to which an information processingdevice according to a modified example 6 of the present disclosure isapplied;

FIG. 21 is a diagram illustrating a connection configuration of abiological information sensing device to which an information processingdevice according to a modified example 7 of the present disclosure isapplied;

FIG. 22 is a diagram illustrating a connection configuration of abiological information sensing device to which an information processingdevice according to a modified example 8 of the present disclosure isapplied; and

FIG. 23 is a diagram illustrating an overview of a biologicalinformation sensing device to which an information processing deviceaccording to a modified example 9 of the present disclosure is applied.

DETAILED DESCRIPTION Underlying Knowledge Forming Basis of PresentDisclosure

In elder care, it is said that keeping records of changes in livingconditions and the body temperature is highly desirable for managingdaily health of an elderly person. There is a plan for a healthmanagement system that periodically measures biological information of ahuman body such as a body temperature and the like, compares themeasured value with a reference value, and makes a notification if achange such as an abnormal feature and the like is detected.

In related art, as means for measuring a body temperature of a humanbody, a thermometer and a radiant heat sensor are known. These meansgrasp the body temperature by making measurement at specific part of ahuman body such as armpit, inner ear, front part of face, and the like.

However, in cases where such contact-type means are used in the healthmanagement system, it is necessary to bring a thermometer into contactwith armpit part to measure the body temperature or to use a specialdevice such as an inner-ear thermometer and the like for bringing intocontact with a human body to measure the body temperature. Therefore, itis necessary to ask a measurement subject to be still or to measure thebody temperature of a measurement subject while being attended by ameasurement operator. As a result, there is an issue of placing burdenon both the measurement operator and the measurement subject.

It is conceivable to apply, to the system performing health management,the method of Japanese Unexamined Patent Application Publication No.2015-55393 where the body temperature measurement is performed using athermal image sensor that measures the body temperature without contact.Here, in Japanese Unexamined Patent Application Publication No.2015-55393, a single body temperature is calculated in the end byadjusting the contribution ratios of respective regions. Thus, a healthmanagement system to which the method of Japanese Unexamined PatentApplication Publication No. 2015-55393 is applied only needs to preparea single reference body temperature for detecting presence or absence ofa change.

However, in a human body with one-sided paralysis, the body temperatureon paralysis side greatly differs from the body temperature ofnon-paralysis side. Therefore, in a case where a single reference bodytemperature is used to detect presence or absence of a change in bodytemperature, when the body temperature on the paralysis side is measuredbecause of the position of a measurement subject at the time ofmeasurement, it is possible to erroneously determine that, for example,the body temperature is normal because a measured body temperature islower than the reference body temperature even though the measurementsubject actually has fever. On the other hand, when the body temperatureon the non-paralysis side is measured because of the position of ameasurement subject at the time of measurement, it is possible toerroneously determine that, for example, the measurement subject hasfever because the measured body temperature is higher than the referencebody temperature even though the measurement subject actually has anormal body temperature.

The present disclosure is made to resolve such issues and to provide atechnique that enables correct grasping of a biological state of ameasurement subject without placing burden on both the measurementsubject and a measurement operator.

An information processing method according to one aspect of the presentdisclosure includes:

-   -   obtaining measurement data for calculating biological        information of a measurement subject from a sensor that performs        measurement without contact;    -   based on content of the measurement data, classifying the        measurement data into a group associated with at least a        position of the measurement subject, calculating the biological        information from classified measurement data, and comparing        calculated biological information with a reference value        associated with a group to which the measurement data belongs;        and    -   making a notification based on the comparing.

According to the present aspect, the measurement data measured by thesensor are classified into the groups based on the positions of ameasurement subject, the reference value associated with the classifiedgroup is compared with the classified measurement data, and anotification is made based on the comparing. Accordingly, a notificationcan be made upon correctly grasping the biological state of ameasurement subject regardless of the position of the measurementsubject at the time of measurement. In other words, the occurrence ofprocessing on an erroneous notification can be reduced. Accordingly,processing load regarding the notification can be reduced. Further,according to the present aspect, the biological information is measuredwithout contact. This enables grasping of the biological state withoutplacing burden on both the measurement subject and the measurementoperator.

In the foregoing aspect, in the classifying of the measurement data, themeasurement data may be classified into the group using a machinelearning model, the machine learning model being a model havingmachine-learned about classification of the measurement data into thegroup based on content of the measurement data, and

-   -   the reference value may be a representative value of the        biological information in the group and is calculated from the        measurement data classified into the group. In this way, the        machine learning model can classify measurement data, thereby        enabling more appropriate classification than a rule-based        classification. Further, the reference value is determined from        the measurement data actually classified. This enables the use        of a more accurate reference value in the foregoing process for        comparing.

In the foregoing aspect, the information processing method may furtherinclude:

-   -   causing the machine learning model to perform machine learning        about classification of the measurement data into the group        based on content of the measurement data, wherein    -   in the machine learning, the representative value of the        biological information in the group may be calculated from        measurement data classified into the group as the reference        value of the group.

According to the present aspect, the representative value of thebiological information of each group is calculated by the machinelearning from the measurement data classified into each group, and thisrepresentative value is calculated as the reference value. This enablescalculation of the reference value for each group suited for ameasurement subject.

In the foregoing aspect, the sensor may be a thermal image sensor,

-   -   the biological information may be a body temperature,    -   the measurement data may be thermal image data obtained by the        thermal image sensor,    -   in the classifying the measurement data, the thermal image data        may be classified into the group based on a characteristic of a        region indicating the measurement subject, the region being        included in the thermal image data obtained by the thermal image        sensor, and    -   in the comparing, the body temperature of the measurement        subject may be calculated from classified thermal image data,        and a calculated body temperature may be compared with a        reference value associated with a group to which the classified        thermal image data belongs.

According to the present aspect, the sensor is constituted by thethermal image sensor, thereby enabling correct grasping of thebiological state of a measurement subject.

In the foregoing aspect, the sensor may be a radio wave sensor,

-   -   the biological information may be an activity amount including        at least one of a body motion value, a breathing rate, and a        heart rate of the measurement subject,    -   the measurement data may be activity amount data indicating the        activity amount obtained by the radio wave sensor,    -   in the classifying the measurement data, the activity amount        data may be classified into the group based on waveform        information of the activity amount data obtained by the radio        wave sensor, and    -   in the comparing, the activity amount of the measurement subject        may be calculated from classified activity amount data, and a        calculated activity amount may be compared with a reference        value associated with a group to which the classified activity        amount data belongs.

According to the present aspect, the sensor is constituted by the radiowave sensor, thereby enabling more accurate detection of at least one ofthe body motion value, the breathing rate, and the heart rate of themeasurement subject.

In the foregoing aspect, in a case where the number of pieces of themeasurement data not corresponding to any one of the groups exceeds areference number, information prompting re-learning of the machinelearning model may be generated.

According to the present aspect, the information prompting re-learningof the machine learning is generated in the case where the number ofpieces of the measurement data not corresponding to any one of thegroups exceeds a reference number during execution of a process forcomparing measurement data and a reference value. Accordingly, forexample, a manager who becomes aware of this information can re-executethe machine learning. In this way, even in the case where themeasurement subject takes a position that has not been observed at thetime of the machine learning during the execution of a process forcomparing measurement data and a reference value.

In the foregoing aspect, the information processing method may furtherinclude

-   -   obtaining time data indicating time of measurement of the        measurement data, wherein    -   in the comparing, whether or not the measurement data is        measured at a predetermined time of day may be determined based        on the time data, and the comparing may be performed when        determined that the measurement data is measured at the        predetermined time of day.

According to the present aspect, a process for comparing is executedwhen measurement data belongs to the predetermined time of day.Accordingly, for example, the process for comparing can be performedusing measurement data measured at time of day in which the state of ameasurement subject is stable, thereby enabling more correct grasping ofthe biological information.

In the foregoing aspect, the position may be a direction of body of themeasurement subject. In this way, measurement data can be classifiedinto a group associated with the direction of body of the measurementsubject. Characteristics of a human body such as the blood flow and thelike change depending on the direction of body. That is, the referencevalue of the biological information such as the body temperature and thelike changes depending on the direction of body. Accordingly, by usingan appropriate reference value, it is possible to determine whether ornot there is abnormality in the measurement subject.

In the foregoing aspect, the notification based on the comparing may bea comparison result between the biological information and the referencevalue or abnormality of the measurement subject. This enables a personwho can receive a notification to be aware of abnormality of ameasurement subject.

The forgoing aspect can be achieved as a method of making constitutingprocessing means to steps. Further, the present disclosure can beachieved as a program that causes a computer to execute steps includedin the method. Further, the present disclosure can be achieved as acomputer-readable recording medium, such as CD-ROM or the like, storingthe program thereon.

EMBODIMENT 1

FIG. 1 is a diagram illustrating an overview of a biological informationsensing device to which an information processing device according toEmbodiment 1 is applied. In FIG. 1, the sensing device 101 includes athermal image sensor and placed in a room such as a bedroom and thelike, where a bed 107 is placed. The sensing device 101 is, for example,placed alongside an air conditioner 105. Preferably, for example, thesensing device 101 is placed in an inconspicuous place such as in thevicinity of a side of the air conditioner 105. For example, the sensingdevice 101 is installed in a room in such a way that a measurement rangeof the sensing device 101 covers the entire area of the bed 107.

The sensing device 101 obtains thermal image data indicating a thermaldistribution in a room in which a human body 106 (one example of themeasurement subject) is included. The sensing device 101 transmits theobtained thermal image data to an information processing device 102 viaa gateway (hereinafter, referred to as GW) 104. The informationprocessing device 102 classifies the thermal image data into respectivegroups based on the position of the human body 106 and manages theclassified thermal image data. The GW 104 may be referred to as arouter.

Here, the room may be, for example, a room in a senior nursing home,where the human body 106 being a caring target stays, or a room in ahouse where the human body 106 lives.

Upon detection of a change in body temperature of the human body 106 bycomparing the thermal image data classified into respective groups andthe thermal image data measured in this time, the information processingdevice 102 transmits alerting information indicating abnormal bodytemperature of the human body 106 to the information display device 103via the GW 104. Upon receipt of the alerting information, theinformation display device 103 issues an alert. This enables a managerof the human body 106 to aware abnormal body temperature of the humanbody 106.

With regard to the manager of the human body 106, for example, acaregiver who takes care of the human body 106 or a manager of a nursinghome may play such role.

FIG. 2 is a diagram illustrating one example of a connectionconfiguration of the biological information sensing device. Asillustrated in FIG. 2, the biological information sensing deviceincludes the sensing device 101, the information processing device 102,the information display device 103, and the GW 104. The sensing device101, the information processing device 102, and the information displaydevice 103 are each connected to the GW 104 via their respectivepredetermined networks. As the predetermined network, for example, awired LAN, a wireless LAN, or a LAN that is a combination thereof may beemployed.

FIG. 3 is a diagram illustrating a configuration of functions of thebiological information sensing device to which an information processingdevice according to Embodiment 1 is applied. The biological informationsensing device includes a sensing section 101A corresponding to thesensing device 101, an information processing section 102A correspondingto the information processing device 102, and an information displaysection 103A corresponding to the information display device 103.

The sensing section 101A (one example of the sensor) includes a sensorsection 301 and a transmission section 302, and measures biologicalinformation without contact. The sensor section 301 is constituted by,for example, a thermal image sensor and obtains thermal image data (oneexample of the measurement data) by capturing an image of the room ofthe human body 106 at predetermined sampling intervals. The transmissionsection 302 is constituted by, for example, a communication circuit of awireless LAN or a wired LAN and transmits the thermal image dataobtained by the sensor section 301 at the predetermined samplingintervals to the information processing section 102A.

The information processing section 102A includes a reception section303, a time data assignment section 304, a data classification section305, a data management section 306, a change detection section 307, atransmission section 308, and a DB section 311.

The reception section 303 (one example of the acquisition section) isconstituted by, for example, a communication circuit of a wireless LANor a wired LAN and receives the thermal image data transmitted from thesensing section 101A at predetermined sampling intervals.

The time data assignment section 304 assigns a measurement time to thethermal image data received at the reception section 303. Here, as themeasurement time, for example, time of obtainment of the thermal imagedata by the reception section 303 is assigned. The measurement time iscomposed of, for example, data indicatingyear/month/day/hour/minute/second.

The data classification section 305 obtains a plurality of the thermalimage data by sequentially obtaining thermal image data to which timedata is assigned by the time data assignment section 304 during alearning phase where learning is performed on groups based on theposition. Further, the data classification section 305 learns about thegroups by classifying the thermal image data based on characteristics ofthe region indicating the human body 106 included in each of theplurality of pieces of obtained thermal image data. As a characteristicof pixel, a contour of a region indicating the human body 106 or atemperature distribution forming this region is employed.

Here, the data classification section 305, for example, learns about thegroups by machine learning. Here, as the machine learning, for example,supervised machine learning or unsupervised machine learning using aneural network may be employed. In the case of supervised machinelearning, the data classification section 305 may learn, for example,weight coefficients of a neural network so as to output a groupidentifier that identifies a group base on the position given to eachthermal image data in advance.

Here, as the position given in advance, for example, the direction ofthe human body 106 may be employed. As the direction of the human body106, for example, at least following four patterns may be employed:“front” indicating the direction of the human body 106 when viewed fromthe front side; “left” indicating the direction of the human body 106when viewed from the left side using the front side as reference;“right” indicating the direction of the human body 106 when viewed fromthe right side using the front side as reference; and “back” indicatingthe direction of the human body 106 when viewed from the back side.Further, as the direction of the human body 106, for example, a depthpattern may be added to each of the four patterns of front, back, left,and right. As the depth pattern, for example, a pattern corresponding toan incremental distance to the thermal image sensor may be employed. Inthis case, as the depth pattern, for example, “close”, “normal”, “far”,and the like may be employed. In the case where there are four patternsof front, back, left, and right for the direction of the human body 106and there are three patterns of “close”, “normal”, and “far” for thedepth of the human body 106, thermal image data is classified intotwelve groups, which is equal to four times three, depending on theposition and the depth. Here, the reason to include the depth in theconsideration is to consider a decrease in measurement accuracy of thethermal image sensor proportional to the distance from the thermal imagesensor to the human body 106. Alternatively, as the direction of thehuman body 106, a direction other than the foregoing four patterns (forexample, diagonally forward right, diagonally forward left, diagonallybackward right, diagonally backward left) may be added, or one or moreof the foregoing four patterns may be omitted.

In the case where unsupervised machine learning is employed, the dataclassification section 305 may employ a deep neural network whose numberof stages is greater than that of a typical neural network. In the casewhere the deep neural network is employed, even without setting theposition and the depth in advance, the data classification section 305can learn about the groups by classifying thermal image data accordingto characteristics of the region indicating the human body 106 includedin the thermal image data.

Alternatively, the data classification section 305 may learn about thegroups using a method other than machine learning. In the case where amethod other than machine learning is employed, the data classificationsection 305 first extracts a region of the human body 106 from thermalimage data. In this case, for example, the data classification section305 may extract a region of the human body 106 by calculating aHistograms of Oriented Gradients (HOG) feature value from thermal imagedata and dividing the thermal image data into the region of the humanbody 106 and the background region based on the HOG feature value. Next,the data classification section 305 may determine the direction of thehuman body 106 by using the contour of the extracted region of the humanbody 106 and a spatial relationship between the nose and the mouth on aface part. Here, the face part can be extracted from the shape of thehuman body 106, and the locations of the nose and the mouth can beacquired from the thermal distribution of the face part. Further, inthis case, as the direction of the human body 106 to be determined,directions determined in advance such as the directions of the foregoingfour patterns of front, back, left, and right may be employed.

Next, the data classification section 305 classifies thermal image databy assigning a group identifier associated with the determined directionto the thermal image data. In this case, the data classification section305 may further classify thermal image data into the foregoingrespective depth patterns based on the size of the face part. In thiscase, the data classification section 305 may classify thermal imagedata by assigning a group identifier, which is determined for eachcombination of the direction and the depth of the human body 106, to thethermal image data.

On the other hand, in a detection phase where a change in biologicalinformation is detected, the data classification section 305sequentially obtains the thermal image data to which time data isassigned by the time data assignment section 304. Further, the dataclassification section 305 classifies the obtained thermal image datainto one of the groups that are learned in the learning phase based onthe characteristics of the region indicating the human body 106 includedin each of the obtained thermal image data. For example, in the casewhere a neural network is employed in the learning phase, the dataclassification section 305 may input thermal image data being adetection target to the neural network in the detection phase, anddetermine the group to which the thermal image data belongs.

Further, in the case where a method other than the foregoing machinelearning is employed in the learning phase, the data classificationsection 305 may use such method to determine the group to which thedetection-target thermal image data belongs.

In the learning phase, the data management section 306 calculates, as areference value of each group, a representative value of the bodytemperature for each of the groups from the thermal image dataclassified in the data classification section 305. Here, it is assumedthat the reference value corresponds to a normal body temperature of thehuman body 106, and an average temperature of the human body for eachgroup is employed as the reference value, the average temperature beingobtained from the thermal image data classified to the group.

Further, the data management section 306 manages the reference values bystoring the calculated reference values of the respective groups in theDB section 311. The data management section 306 may update the referencevalue based on a classification result of thermal image data even in thedetection phase.

The change detection section 307 (one example of the comparison section)detects presence or absence of a change in body temperature of the humanbody 106 by, in the detection phase, calculating a body temperature (oneexample of the biological information) of the human body 106 fromthermal image data classified to one of the groups by the dataclassification section 305 and comparing the calculated body temperaturewith the reference value associated with the group to which the thermalimage data belongs. Further, when a change in body temperature isdetected, the change detection section 307 generates alertinginformation indicating abnormal body temperature. Here, it is assumedthat the reference value corresponds to a normal body temperature of thehuman body 106. Thus, for example, the change detection section 307 maydetermine that there is a change in body temperature if a temperaturedifference between the body temperature calculated from target thermalimage data and the normal body temperature indicated as the referencevalue is equal to or larger than plus or minus one degree Celsius. Here,it is determined that there is a change in body temperature if there isthe temperature difference equal to or larger than plus or minus onedegree Celsius. However, the present disclosure is not limited theretoand may alternatively determine that there is a change in bodytemperature if there is the temperature difference equal to or largerthan a predetermined temperature other than the one degree Celsius (forexample, 0.5 degrees Celsius, 1.5 degrees Celsius, and the like).

Here, the change detection section 307 may detect part (for example,face part) designated by a comparison part parameter 311C, which will bedescribed below, from thermal image data, and calculate, for example, anaverage temperature of the body temperature of the human body 106 at thedetected part.

The transmission section 308 is constituted by, for example, acommunication circuit of a wireless LAN or a wired LAN and transmitsalerting information to the information display section 103A when thechange detection section 307 generates the alerting information.

The database (hereinafter, referred to as DB) section 311 is constitutedby, for example, a non-volatile storage device and stores thereinvarious data that the data management section 306 manages. The DBsection 311 stores therein classification parameters 311A,classification data 311B, and comparison part parameters 311C.

The classification parameters 311A are, for example, weight coefficientsof a neural network obtained as a result of learning in the case wherethe neural network is employed as the learning phase.

The classification data 311B is thermal image data classified into therespective groups by the data classification section 305 in the learningphase and the detection phase.

The comparison part parameter 311C is a parameter indicating part of thehuman body 106, which is used as a comparison target at the time ofdetection by the change detection section 307. For example, as thecomparison part parameter, information designating a face is employedwhen the face is used as the comparison target.

In the case where supervised machine learning is employed, the DBsection 311 may store therein information indicating the position andthe depth that serves as a classification target determined in advance.

The information display section 103A is constituted by, for example, acomputer available for the manager of the human body 106, furtherincludes a reception section 309 and a display section 310, and outputsalerting information. Alternatively, the information display section103A may be constituted by a desktop computer or by a tablet terminal ora portable computer such as a smartphone and the like.

The reception section 309 is constituted by a communication circuit of awireless LAN or a wired LAN and receives alerting informationtransmitted from the information processing section 102A. The displaysection 310 is constituted by, for example, a liquid crystal display oran organic EL display, and displays the alerting information. In thiscase, the display section 310 may display, for example, a message or thelike indicating that the body temperature of the human body 106 is notnormal. Alternatively, the information display section 103A may output,from a loudspeaker not illustrated in the figure, a sound messageindicating that the body temperature of the human body 106 is notnormal.

Next, a data processing method in the information processing section102A is described with reference to FIG. 4 to FIG. 6. FIG. 4 is adiagram illustrating a data processing method in the learning phase.FIG. 5 is a diagram illustrating a data processing method in thedetection phase. FIG. 6 is a diagram that follows FIG. 5 and illustratesthe data processing method in the detection phase.

Referring to FIG. 4, when the biological information sensing device isinstalled in a site, the information processing section 102A generatesthe classification parameters 311A by learning thermal image data for acertain period of time. First, the time data assignment section 304assigns time data (402) to each of thermal image data (401) obtained bythe sensing section 101A. Next, the data classification section 305performs, for each of the thermal image data (403) to which time data isassigned, a mask process to mask the region whose temperature range isother than a predetermined temperature range (for example, 25 degreesCelsius to 45 degrees Celsius). Next, by performing machine learningusing a neural network, the data classification section 305 classifiesthermal image data into each group of the human body 106 and generatesthe classification parameters 311A (404). Here, as the predeterminedtemperature range, the range from 25 degrees Celsius to 45 degreesCelsius is employed. However, for example, by employing the range from30 degrees Celsius to 45 degrees Celsius or the like, the differencebetween the human body 106 and an object (for example, furniture or thelike) other than the human body 106 may be made clearer. It is assumedthat the predetermined temperature range corresponds to a temperaturerange that the human body 106 can have.

Next, referring to FIG. 5, when the learning phase ends, the detectionphase starts. First, as in the learning phase, the time data assignmentsection 304 assigns time data (402) to thermal image data (501) obtainedby the sensing section 101A. Next, as in the learning phase, the dataclassification section 305 performs a mask process on the thermal imagedata (503), to which time data is assigned, to mask the region whosetemperature range is other than a predetermined temperature range. Next,by inputting, to the neural network generated in the learning phase, thethermal image data in which the region whose temperature range is otherthan the predetermined temperature range is masked, the dataclassification section 305 classifies the thermal image data into one ofthe groups having been classified in the learning phase (504). Here, asthe groups, there are the first to the Nth groups. Thus, the thermalimage data is classified to one of the first to Nth groups (N is aninteger equal to or larger than one). In this way, the detection-targetthermal image data is classified according to characteristics of aregion indicating the human body 106. The classified thermal image datais stored in the DB section 311 as the classification data 311B. Notethat the neural network generated by the learning phase is one exampleof a machine learning model.

Next, referring to FIG. 6, the change detection section 307 readsthermal image data being a detection target from the classification data311B, and detects part designated by the comparison part parameter 311Cfrom the read thermal image data (601). For example, if the comparisonpart parameter 311C designates a face part, the change detection section307 detects a face part from thermal image data being a detectiontarget.

Next, the change detection section 307 detects presence or absence of achange in body temperature of the human body 106 (602) by calculating abody temperature of the part detected from the thermal image data andcomparing the calculated body temperature with the reference valueassociated with the group to which the thermal image data belongs. Here,the change detection section 307 determines that there is a change inbody temperature if a temperature difference between the calculated bodytemperature and the normal body temperature indicated as the referencevalue is equal to or larger than plus or minus one degree Celsius.

In the example of FIG. 6, the classification data 311B are divided intothe first to Nth groups depending on the position and the depth.Accordingly, there are also N reference values associated with the firstto Nth groups.

In the present embodiment, as the reference value, a value calculated inthe learning phase is employed. However, the present disclosure is notlimited thereto, and a value determined in advance may alternatively beemployed for each group. Alternatively, as the reference value, a valuecalculated for each group from thermal image data classified during acertain period of time from present to past may be employed.

FIG. 7 is a flowchart illustrating one example of a process of theinformation processing device 102 in the detection phase. For example,the flowchart of FIG. 7 is executed periodically at a predeterminedsampling frequency at which the sensing section 101A obtains thermalimage data.

First, the data classification section 305 obtains thermal image data towhich time data is assigned by the time data assignment section 304(S701). Next, the data classification section 305 performs the maskprocess on the obtained thermal image data to mask part other than 25degrees Celsius to 45 degrees Celsius (S702).

Next, the data classification section 305 classifies the thermal imagedata subjected to the mask process by inputting the thermal image datasubjected to the mask process to the neural network for which theclassification parameters 311A are generated in the learning phase(S703). This allows thermal image data to be classified into one of thefirst to Nth groups according to the position and the depth. It ispossible that thermal image data cannot be classified to any one of thefirst to Nth groups. For example, it is a case where the human body 106takes a position and a depth in the detection phase, which have not beentaken in the learning phase. In this case, this thermal image data isclassified to an “other group”.

Next, the data classification section 305 determines whether or not thenumber of pieces of thermal image data classified to the other groupexceeds a reference number (S704).

If the number of pieces of thermal image data classified to the othergroup exceeds the reference number (Yes in S704), a flag is generated toprompt re-execution of the learning phase (S705). Here, for example, thegenerated flag is transmitted to the information display section 103A.The information display section 103A displays an image indicatinggeneration of a flag on the display section 310. This prompts themanager to execute the learning phase again. The manager inputs, forexample, a re-learning command to the information processing section102A using an input device not illustrated in the figure. Then, the dataclassification section 305 executes the foregoing learning phase usingall the thermal image data accumulated as the classification data 311Bto date, and generates the classification parameters 311A again. Thisallows new learning to be executed on a group including a position and adepth of the human body 106 indicated by the thermal image dataclassified to the other group. As a result, a group based on theposition and the depth of the human body 106 taken for the first time inthe detection phase is added as a classification target group. Notethat, when the learning phase is re-executed, reference values of therespective groups are calculated from thermal image data classified intothe respective groups. Thus, with regard to thermal image dataclassified to the newly added group, a temperature change is detected bycomparing the thermal image data with a corresponding reference value.

Next, the change detection section 307 detects part designated by thecomparison part parameter 311C from thermal image data being a detectiontarget, and calculates the body temperature of the human body 106 usingthe pixel value at the detected part (S706).

Next, the change detection section 307 compares the reference valueassociated with the group of the thermal image data being a detectiontarget with the body temperature of the human body 106 calculated instep S706, and generates alerting information (S709) if the temperaturedifference between the body temperature of human body 106 and thereference value is equal to or larger than plus or minus one degreeCelsius (Yes in S708). When step S709 ends, the process proceeds to stepS710. The generated alerting information is transmitted to theinformation display section 103A, and an alert is issued to the manager.On the other hand, if the temperature difference between the bodytemperature of the human body 106 and the reference value is less thanplus or minus one degree Celsius (No in S708), the process proceeds tostep S710.

In step S710, using the classification data 311B to which thermal imagedata being a detection target is classified, the data management section306 updates the reference value of the group to which the thermal imagedata belongs, and the process returns to step S701. For example, if thethermal image data being a detection target is classified to the firstgroup, the data management section 306 may update the reference value ofthe first group.

As described above, according to the information processing device 102of Embodiment 1, an abnormal body temperature of the human body 106 isdetected by classifying thermal image data into the groups based on theposition and the depth and comparing the reference value associated withthe group with the body temperature calculated from the thermal imagedata. Accordingly, the information processing device 102 enables precisedetection of presence or absence of abnormal body temperature of thehuman body 106 regardless of the position and the depth of the humanbody 106 at the time of measurement. Further, the information processingdevice 102 enables measurement of the body temperature of the human body106 without contact, thereby making it possible to detect abnormal bodytemperature without placing burden on both the human body 106 and themanager of the human body 106. Further, in the present embodiment, thereference value is calculated from thermal image data obtained bycapturing an image of the human body 106. This enables calculation of areference value appropriate for the human body 106.

EMBODIMENT 2

FIG. 8 is a diagram illustrating an overview of a biological informationsensing device to which an information processing device according toEmbodiment 2 is applied. The biological information sensing deviceaccording to Embodiment 2 employs a sensing device 901_A including aradio wave sensor. In the present embodiment, no description is providedfor a constituting element identical to that of Embodiment 1.

The sensing device 901_A includes a radio wave sensor and is installedinside a room such as a bedroom or the like, as in FIG. 1. Here, forexample, the sensing device 901_A is installed in a room in such a waythat the measurement range of the sensing device 901_A covers the entirearea of the bed 107. The sensing device 901_A obtains activity amountdata of the human body 106. The sensing device 901_A transmits theobtained activity amount data to an information processing device 902via a GW 104. The information processing device 902 classifies theactivity amount data into respective groups based on the position of thehuman body 106 and manages the classified activity amount data.

Upon detecting a change in activity amount (one example of thebiological information) of the human body 106 by comparing activityamount data classified into respective groups and activity amount datameasured in this time, the information processing device 902 notifiesthe information display device 103 of alerting information indicatingabnormal activity amount of the human body 106 via the GW 104. Uponreceipt of the alerting information, the information display device 103issues an alert. This enables the manager of the human body 106 to awareabnormal activity amount of the human body 106.

FIG. 9 is a diagram illustrating one example of a functionalconfiguration of a biological information sensing device to which aninformation processing device according to Embodiment 2 is applied. Thebiological information sensing device includes a sensing section 901A(one example of the sensor) corresponding to the sensing device 901_A,an information processing section 902A corresponding to the informationprocessing device 902, and an information display section 103Acorresponding to the information display device 103. The sensing section901A differs from that of FIG. 3 in that the sensing section 901Aincludes a sensor section 901.

The sensor section 901 is constituted by, for example, a two-channelDoppler-type radio wave sensor, and obtains activity amount data of thehuman body 106 by emitting a radio wave to the human body 106 atpredetermined sampling intervals and receiving a return wave from thehuman body 106. As the radio wave, for example, a microwave in a 24 GHzband may be employed.

Alternatively, as the sensor section 901, a radio wave sensor other thanthat of Doppler type may be employed. For example, a radio wave sensorof Frequency Modulated Continuous Wave (FMCW) type and the like may beemployed.

In the learning phase, the data classification section 905 learns aboutgroups based on the position of the human body 106 by obtaining aplurality of pieces of activity amount data to which time data isassigned by the time data assignment section 304 and classifying theobtained plurality of pieces of activity amount data based on waveforminformation.

In the case where the sensor section 901 is constituted by a two-channelDoppler-type radio wave sensor, the distance to the human body 106varies depending on the amplitude of activity amount data, and the speedof body motion varies depending on the change in frequency of theactivity amount data. Accordingly, in this case, as the waveforminformation, the amplitude and the frequency are employed.

In the case where the sensor section 901 is constituted by a FMCW-typeradio wave sensor, the distance to the human body 106 varies dependingon the amplitude and the phase of activity amount data, and the speed ofbody motion varies depending on the change in phase of the activityamount data. Accordingly, in this case, as the waveform information, theamplitude and the phase are employed.

Further, the speed of body motion changes depending on the position ofthe human body 106 such as lying on one's back, lying on one's stomach,or lying on one's side, and the speed of body motion varies depending onthe state of the human body 106 such as being asleep or being awake.Accordingly, classifying activity amount data based on the waveforminformation enables to classify the activity amount data into respectivegroups of the position, the state, and the distance of the human body106, thereby enabling learning about the group for each position, state,and distance of the human body 106.

In the present embodiment, the data classification section 905 may learnabout the groups using the machine learning described in Embodiment 1.

In the detection phase where a change in biological information isdetected, the data classification section 905 classifies, to one of thegroups learned in the learning phase, activity amount data to which timedata is assigned by the time data assignment section 304 based on thewaveform information. Specifically, the data classification section 905may classify activity amount data by inputting the activity amount datato a neural network generated in the learning phase.

In the learning phase, the data management section 906 calculates, as areference value of each group, a representative value of activity amountfor each of the groups from the activity amount data classified intoeach group by the data classification section 905. Here, as thereference value, reference values of a body motion value, a heart rate,and a breathing rate can be employed. With regard to the activity amountdata, frequency bands of the body motion value, the heart rate, and thebreathing rate are known in advance. Thus, the body motion value, theheart rate, and the breathing rate can be detected from values of therespective frequency bands. Accordingly, the data management section 906may calculate an average value of each group of the body motion values,the heart rates, and the breathing rates, as the reference value of eachgroup of the body motion values, the heart rates, and the breathingrates. The data management section 306 may update the reference valuebased on a classification result of activity amount data even in thedetection phase.

The change detection section 907 detects presence or absence of a changein activity amount of the human body 106 by, in the detection phase,calculating an activity amount including a body motion value, abreathing rate, and a heart rate of the human body 106 from the activityamount data classified to one of the groups by the data classificationsection 905 and comparing the calculated body temperature with thereference value associated with the group to which the thermal imagedata belongs. Further, when a change in activity amount is detected, thechange detection section 907 generates alerting information indicatingabnormal activity amount. Here, it is assumed that the reference valuecorresponds to an activity amount of the human body 106 in normal times.Thus, the change detection section 907 may determine that there is achange in activity amount if a difference between the reference valueand the activity amount indicated by a target activity amount data isequal to or larger than a plus or minus a predetermined value.

The DB section 911 stores therein classification parameters 911A,classification data 911B, and comparison value calculation parameters911C. The classification parameters 911A are, for example, weightcoefficients of a neural network obtained as a result of learning ofactivity amount data in the learning phase. The comparison valuecalculation parameter 911C is a parameter defining content of acomparison value to be detected from activity amount data at the time ofdetection by the change detection section 907. In the presentembodiment, the body motion value, the pulse rate, and the heart rateare employed as the reference values. Thus, as the comparison valuecalculation parameter 911C, for example, information indicating each ofthe body motion value, the pulse rate, and the heart rate is employed.Alternatively, as the comparison value calculation parameter 911C, forexample, a frequency band in activity amount data for each of the bodymotion value, the pulse rate, and the heart rate may be employed.

In the case where supervised machine learning is employed, the DBsection 911 may store therein information relating to groups beingclassification targets determined in advance (information indicatingposition (lying on one's back, lying on one's stomach, or the like),state (being asleep, being awake, or the like), distance).

Next, a data processing method in the information processing section902A is described with reference to FIG. 10 to FIG. 12. FIG. 10 is adiagram illustrating the data processing method in the learning phase.FIG. 11 is a diagram illustrating the data processing method in thedetection phase. FIG. 12 is a diagram that follows FIG. 11 andillustrates the data processing method in the detection phase.

Referring to FIG. 10, when the biological information sensing device isinstalled in a site, the information processing section 902A generatesthe classification parameters 911A by learning activity amount data.First, the time data assignment section 304 assigns time data (1002) toeach of activity amount data (1001) obtained by the sensing section901A.

Next, by performing machine learning using a neural network, the dataclassification section 905 classifies the activity amount data (1003),to which time data is assigned, into the groups and generates theclassification parameters 911A (1004).

Next, referring to FIG. 11, when the learning phase ends, the detectionphase starts. As in the learning phase, the time data assignment section304 assigns time data (1002) to activity amount data (1101) obtained bythe sensing section 901A. Next, as in the learning phase, by inputting,to the neural network generated in the learning phase, the activityamount data (1103) to which time data is assigned, the dataclassification section 905 classifies the activity amount data into oneof the groups having been classified in the learning phase (1104). Inthis way, the detection-target activity amount data is classifiedaccording to waveform information. The classified activity amount datais stored in the DB section 911 as the classification data 911B.

Next, referring to FIG. 12, the change detection section 907 readsactivity amount data, which becomes a detection target, from theclassification data 911B, and detects a comparison value designated bythe comparison value calculation parameter 911C from the read activityamount data (1201). Here, as the comparison value, the body motionvalue, the pulse rate, and the heart rate are employed. Accordingly, abody motion value, a pulse rate, and a heart rate are extracted from theread activity amount data.

Next, the change detection section 907 detects presence or absence of achange in activity amount of the human body 106 (1202) by comparing thebody motion value, the pulse rate, and the heart rate detected from theactivity amount data with the respective reference values associatedwith the group to which the activity amount data is classified. Here,the change detection section 907 determines that there is a change inactivity amount if each of differences between the detected body motionvalue, pulse rate, and heart rate and their respective reference valuesis equal to or larger than plus or minus a predetermined value.

In the example of FIG. 12, the classification data 911B is classifiedinto N groups consisting of the first to Nth groups (N is an integerequal to or larger than one). Accordingly, there are also N referencevalues associated with the first group to the Nth group. Although, inthe example of FIG. 12, a reference value for one of the body motionvalue, the heart rate, and the pulse rate is illustrated in the figureas the reference value, reference values for respective ones of the bodymotion value, the heart rate, and the pulse rate are included in anactual case.

In the present embodiment, as the reference value, a value calculated inthe learning phase is employed. However, the present disclosure is notlimited thereto, and a value determined in advance may alternatively beemployed for each group. Alternatively, as the reference value, a valuecalculated from activity amount data classified during a certain periodof time from present to past may be employed.

In Embodiment 2, the flowchart is the same as that of FIG. 7 except thatthe thermal image data is replaced with the activity amount data. Thus,the description thereof is not repeated.

As described above, in Embodiment 2, an abnormal activity amount isdetected by classifying activity amount data into each group includingthe position, the state, and the distance according to waveforminformation of the activity amount data, and comparing an activityamount of the classified activity amount data with the reference valueof the associated group. Accordingly, in Embodiment 2, presence orabsence of abnormal activity amount of the human body 106 can bedetected precisely regardless of the position of the human body 106 atthe time of measurement. Further, according to the present aspect, thebiological information is measured without contact. This enablesdetection of abnormal activity amount without placing burden on both thehuman body 106 and a measurement operator.

The information processing device according to the present disclosuremay adopt the following modified examples.

MODIFIED EXAMPLE 1

The change detection section 307 may determine whether or notmeasurement data is the measurement data measured at predetermined timeof day based on time data assigned to the measurement data, and performsa process for detecting a change if the measurement data is themeasurement data measured at the predetermined time of day. Here, as thepredetermined time of day, the time of day at which the activity amountof the human body 106 is stable is employed. For example, a certainperiod immediately after waking up, a certain period immediately beforewaking up, a certain period immediately before going to bed, or acertain period immediately after falling to sleep may be employed.

In this case, the data classification section 305 may classify onlymeasurement data measured at the predetermined time of day. In this way,the reference value becomes a value calculated from the measurement dataof the predetermined time of day. This enables precise detection of achange in biological information.

Alternatively, the data classification section 305 may classifymeasurement data while considering time of day, in addition to theposition, the state, or the distance. In this case, a change inbiological information can be detected precisely in consideration of theactivity amount of the human body 106, which varies depending on time ofday.

MODIFIED EXAMPLE 2

FIG. 13 is a diagram illustrating an overview of a biologicalinformation sensing device to which an information processing deviceaccording to a modified example 2 of the present disclosure is applied.In Embodiment 1, as illustrated in FIG. 1, the sensing device 101 isconfigured as a unit separated from the air conditioner 105. However, inthe modified example 2, as illustrated in FIG. 13, a sensing device101_B is included in an air conditioner 105B. In the example of FIG. 13,the sensing device 101_B is installed on the front face of the airconditioner 1058 in such a way that a sensor face faces the human body106.

In the modified example 2, the information processing device 102 may be,for example, constituted by a cloud server or by a server installed in alocal place.

MODIFIED EXAMPLE 3

FIG. 14 is a diagram illustrating a configuration of functions of abiological information sensing device to which an information processingdevice according to a modified example 3 of the present disclosure isapplied. In Embodiment 1, the information processing section 102A isconfigured as a unit separated from the sensing section 101A. However,in the modified example 3, as illustrated in FIG. 14, the sensingsection 101B and the information processing section 102A illustrated inFIG. 3 are configured as a unified unit. In the modified example 3, theinformation processing device is constituted by the sensing section101B.

In the modified example 3, the sensing section 101A and the informationprocessing section 102A are unified. Thus, the transmission section 302and the reception section 303 that are present in FIG. 3 are omitted. Inthe modified example 3, the sensing section 101B may be constituted by asensor unit built inside the air conditioner 105 or a dedicated sensorunit provided in a unit separated from the air conditioner 105.

FIG. 15 is a diagram illustrating one example of a connectionconfiguration of a biological information sensing device to which aninformation processing device according to the modified example 3 of thepresent disclosure is applied. A sensing device 101_B corresponds to thesensing section 101B illustrated in FIG. 14. The sensing device 101_B isconnected to the information display device 103 via the GW 104.

MODIFIED EXAMPLE 4

FIG. 16 is a diagram illustrating a configuration of functions of abiological information sensing device to which an information processingdevice according to a modified example 4 of the present disclosure isapplied. In Embodiment 1, the time data assignment section 304 isprovided in the information processing section 102A. However, in themodified example 4, the time data assignment section 304 is provided ina sensing section 101C. The time data assignment section 304 is arrangedbetween the sensor section 301 and the transmission section 302. On theother hand, an information processing section 102C does not include thetime data assignment section 304. In the modified example 4, a processfor assigning time data is performed in the sensing section 101C,thereby reducing processing load of the information processing section102C. In the modified example 4, the sensing section 101C is constitutedby a high-performance sensor built inside the air conditioner 105 or bya high-performance sensor configured as a unit separated from the airconditioner 105.

MODIFIED EXAMPLE 5

FIG. 17 is a diagram illustrating a configuration of functions of abiological information sensing device to which an information processingdevice according to a modified example 5 of the present disclosure isapplied. In the example of FIG. 17, the information processing section102A is divided into an information processing section 102D includingthe time data assignment section 304 and an information processingsection 102E.

The information processing section 102D is constituted by, for example,a high-performance sensor built in the air conditioner 105 or a computerfor home use. The information processing section 102E is constituted by,for example, a cloud server. The information processing section 102Dincludes a reception section 303D for communicating with a sensingsection 101D and a transmission section 302D for communicating with theinformation processing section 102E. The information processing section102E includes the reception section 303 for communicating with theinformation processing section 102D. FIG. 18 is a diagram illustrating afirst example of a connection configuration of a biological informationsensing device to which an information processing device according tothe modified example 5 of the present disclosure is applied. Aninformation processing device 102_D corresponding to the informationprocessing section 102D is connected to an information processing device102_E corresponding to the information processing section 102E and theinformation display device 103 via the GW 104. The informationprocessing device 102_D is also connected to a sensing device 101_Dcorresponding to the sensing section 101D.

FIG. 19 is a diagram illustrating a second example of the connectionconfiguration of a biological information sensing device to which aninformation processing device according to the modified example 5 of thepresent disclosure is applied. In the second example, the sensing device101_D, the information processing device 102_D, the informationprocessing device 102_E, and the information display device 103 areconnected to each other via the GW 104.

MODIFIED EXAMPLE 6

FIG. 20 is a diagram illustrating a connection configuration of abiological information sensing device to which an information processingdevice according to a modified example 6 of the present disclosure isapplied. In the modified example 6, the GW 104 is connected to theinformation processing device 102 and the information display device 103via a public communication network 801. The sensing device 101 isconnected to the GW 104.

MODIFIED EXAMPLE 7

FIG. 21 is a diagram illustrating a connection configuration of abiological information sensing device to which an information processingdevice according to a modified example 7 of the present disclosure isapplied. The modified example 7 is an example in which the biologicalinformation sensing device according to the modified example 5 isconfigured using the public communication network 801. The GW 104 isconnected to the information processing device 102_E and the informationdisplay device 103 via the public communication network 801. The sensingdevice 101 is connected to the GW 104 via the information processingdevice 102_D.

MODIFIED EXAMPLE 8

FIG. 22 is a diagram illustrating a connection configuration of abiological information sensing device to which an information processingdevice according to a modified example 8 of the present disclosure isapplied. The modified example 8 is an example in which the biologicalinformation sensing device according to the modified example 5 isconfigured using the public communication network 801.

The GW 104 is connected to the information processing device 102_E andthe information display device 103 via the public communication network801. Each of the sensing device 101 and the information processingdevice 102_D is connected to the GW 104 via a LAN.

MODIFIED EXAMPLE 9

FIG. 23 is a diagram illustrating an overview of a biologicalinformation sensing device to which an information processing deviceaccording to a modified example 9 of the present disclosure is applied.The modified example 9 is an example in which the sensing device 901_Ais placed under the bed 107 in the biological information sensing deviceaccording to Embodiment 2. Even in the case where the sensing device901_A is placed under the bed 107, the activity amount data can bemeasured as in Embodiment 2.

MODIFIED EXAMPLE 10

In Embodiment 2, the body motion value, the heart rate, and the pulserate are employed as the activity amount. Alternatively, at least one ofthose may be employed as the activity amount.

MODIFIED EXAMPLE 11

In Embodiment 1, it is described that the change detection section 307generates the alerting information indicating abnormal body temperatureof the human body 106. However, the present disclosure is not limitedthereto. For example, the change detection section 307 may generate, asa comparison result, a difference between the body temperature of thehuman body 106 calculated from thermal image data and the referencevalue, and transmits information indicating the difference to theinformation display section 103A via the transmission section 308.

MODIFIED EXAMPLE 12

In Embodiment 2, it is described that the change detection section 907generates the alerting information indicating abnormal activity amountof the human body 106. However, the present disclosure is not limitedthereto. The change detection section 907 may generate, as a comparisonresult, a difference between the activity amount of the human body 106calculated from activity amount data and the reference value, andtransmits information indicating the difference to the informationdisplay section 103A via the transmission section 308. Since theactivity amount data includes the body motion value, the breathing rate,and the heart rate, the change detection section 907 may include in thecomparison result the differences from the reference values forrespective ones of the body motion value, the breathing rate, and theheart rate.

The modified examples 1 to 8 may be applicable to both Embodiment 1 andEmbodiment 2.

The information processing devices and the information processingmethods according to respective embodiments of the present disclosureenable the obtainment of biological information in a non-invasive mannerto human body, and further enable appropriate grasping of a biologicalstate even if the position of a measurement target changes, therebymaking them useful for daily recording of biological information anddetection of a change in elder care and for controlling an airconditioner in conjunction with a body motion state.

What is claimed is:
 1. An information processing method comprising:obtaining measurement data for calculating biological information of ameasurement subject from a sensor that performs measurement withoutcontact; based on content of the measurement data, classifying themeasurement data into a group associated with at least a position of themeasurement subject, calculating the biological information fromclassified measurement data, and comparing calculated biologicalinformation with a reference value associated with a group to which themeasurement data belongs; and making a notification based on a result ofthe comparing.
 2. The information processing method according to claim1, wherein in the classifying of the measurement data, the measurementdata is classified into the group using a machine learning model, themachine learning model being a model having machine-learned aboutclassification of the measurement data into the group based on contentof the measurement data, and in the machine learning, the referencevalue is a representative value of the biological information in thegroup and is calculated from the measurement data classified into thegroup.
 3. The information processing method according to claim 2,further comprising: causing the machine learning model to performmachine learning about classification of the measurement data into thegroup based on content of the measurement data, wherein in the machinelearning, the representative value of the biological information in thegroup is calculated from measurement data classified into the group asthe reference value of the group.
 4. The information processing methodaccording to claim 1, wherein the sensor is a thermal image sensor, thebiological information is a body temperature, the measurement data isthermal image data obtained by the thermal image sensor, in theclassifying the measurement data, the thermal image data is classifiedinto the group based on a characteristic of a region indicating themeasurement subject, the region being included in the thermal image dataobtained by the thermal image sensor, and in the comparing, the bodytemperature of the measurement subject is calculated from classifiedthermal image data, and a calculated body temperature is compared with areference value associated with a group to which the classified thermalimage data belongs.
 5. The information processing method according toclaim 1, wherein the sensor is a radio wave sensor, the biologicalinformation is an activity amount including at least one of a bodymotion value, a breathing rate, and a heart rate of the measurementsubject, the measurement data is activity amount data indicating theactivity amount obtained by the radio wave sensor, in the classifyingthe measurement data, the activity amount data is classified into thegroup based on waveform information of the activity amount data obtainedby the radio wave sensor, and in the comparing, the activity amount ofthe measurement subject is calculated from classified activity amountdata, and a calculated activity amount is compared with a referencevalue associated with a group to which the classified activity amountdata belongs.
 6. The information processing method according to claim 3,wherein in a case where number of pieces of the measurement data notcorresponding to any one of the groups exceeds a reference number,information prompting re-learning of the machine learning model isgenerated.
 7. The information processing method according to claim 1,further comprising: obtaining time data indicating time of measurementof the measurement data, wherein in the comparing, whether or not themeasurement data is measured at a predetermined time of day isdetermined based on the time data, and the comparing is performed whendetermined that the measurement data is measured at the predeterminedtime of day.
 8. The information processing method according to claim 1,wherein the position is a direction of body of the measurement subject.9. The information processing method according to claim 1, wherein thenotification based on the comparing is a comparison result between thebiological information and the reference value or abnormality of themeasurement subject.
 10. An apparatus comprising: a sensor that measuresmeasurement data for calculating biological information of a measurementsubject without contact; a processor; and a memory storing thereon acomputer program, which when executed by the processor, causes theprocessor to perform operations including: obtaining the measurementdata from the sensor; based on content of the measurement data,classifying the measurement data into a group associated with at least aposition of the measurement subject; calculating the biologicalinformation from classified measurement data, and comparing calculatedbiological information with a reference value associated with a group towhich the measurement data belongs; and making a notification based on aresult of the comparing.
 11. A non-transitory recording medium storingthereon a computer program, which when executed by the processor, causesthe processor to perform operations according to claim 1.